Executives are wrestling with a paradox around AI adoption.
On the one hand, the maxim of “garbage in, garbage out” has continued to serve as a vital differentiator between expensive AI experiments and ROI-yielding success. In other words, data leaders get that tomorrow’s AI use cases are highly dependent on a steady supply of clean, quality, well-governed data.
On the other, research shows that more and more organizations are in fact leaning on AI technologies to solve those exact data challenges, with at least 94% of the most data mature organizations already using AI for governance, compliance, data cleaning, and, and workflow automation purposes.
Decision makers are thus left with a perplexing question: “Do we need to clean up our data before we can reliably put AI to work, or is AI actually the solution to our data quality woes?”
That paradox is dissolving fast. With the rise of advanced cloud-native platforms like the Snowflake AI Data Cloud and the growing sophistication of applied AI solutions, organizations no longer need to wait until every dataset is pristine before exploring AI’s potential.
This is in no small part thanks to the growing role of data and AI consulting in expanding the art of the possible: accelerating both data modernization and AI roadmaps at the same time.
Why Data Fundamentals Still Matter
Even with AI’s growing ability to wrangle unstructured and messy data, no enterprise succeeds with AI on a shaky foundation. Value-generating AI adoption (the only kind that matters) requires:
- Unified, high-quality data: Clean, governed, and centralized data sets form the bedrock for training reliable models.
- Scalable infrastructure: Elastic platforms like Snowflake allow enterprises to process massive data volumes at speed. This capability is directly tied to the rise of cloud and hybrid data architectures.
- Governance and compliance: Clear lineage, access controls, and auditability protect against risk in regulated industries. A new maxim is emerging around this point: successful AI is explainable AI.
- Domain-specific context: AI models are only as good as the context they’re designed for. Nuanced industries like finance, healthcare, retail, and pharma each come with their own ideas of what “data readiness” looks like.
Without these fundamentals, AI risks becoming fragmented experimentation rather than an enterprise-wide value driver.
The New Era: Using AI to Get AI-Ready
The rules of the data modernization game are already changing, however. AI is no longer just the end goal of major modernization efforts as it becomes increasingly part of the toolkit enterprises are using to get there.
This shift is most apparent in the data and AI consulting space, where firms like Hakkoda, now part of IBM, are bringing their own AI-powered accelerators to some of their clients’ most persistent data modernization challenges. This AI-augmented approach allows their customers to cut through months of manual work and reduce the risk of stalled initiatives.
Instead of waiting for data perfection or crossing their fingers that internal AI solutions will be reliable and explainable enough to help get their data houses in order, organizations are now positioned to start generating value immediately, using AI to boost data quality and insight while building toward long-term governance, compliance, and architectural goals.
How Data and AI Consulting Unlocks Value
Most enterprises struggle not with whether to adopt AI, but with how to adopt it responsibly, effectively, and at scale. Data and AI consulting firms like Hakkoda introduce value to a business by helping them answer that question and see adoption through to the end.
At Hakkoda, we help enterprises:
- Assess data readiness: Benchmarking existing data estates, silos, and gaps.
- Deploy modern data platforms: Implementing scalable, AI-native foundations like Snowflake.
- Apply AI to accelerate transformation: Leveraging AI for everything from data pipeline automation to predictive analytics.
- Operationalize responsibly: Embedding governance, explainability, and compliance into every AI workflow.
By bridging the technical expertise of modern data engineering with the industry-sensitive business acumen of applied AI strategy, we help clients not just experiment with AI, but integrate reliable, explainable solutions into the core of their operations.
Moving Beyond the Paradox
The age of choosing between manually fixing data issues or rushing AI adoption is over.
Today’s leading enterprises have embraced the new reality that these two urgent strategic needs can and should evolve together—strengthening the other in a cycle of compounding returns.
By selecting the right data and AI consulting partner, organizations can collapse the timeline from experimental pilot projects to accountable and compliant enterprise-wide adoption.
Because in this new era, the winners won’t just be those who use AI. The winners will be those who design their enterprises to grow alongside it.
Ready to lead your industry in this exciting new chapter? Talk to one of our experts today.
